“What most people think of as ‘artificial intelligence’ -- even deep learning technologies -- fail to go beyond basic pattern recognition to replicate intelligent, decision-making skills,” said Dr. Itamar Arel, CEO of Osaro. “Our deep reinforcement learning approach allows human mentors to teach computers and robots how to take intelligent actions in response to new goals, and new situations.”

State of the Technology

Traditional approaches to machine learning, including deep learning, focus on pattern recognition, like transcribing speech or identifying objects in an image or a video. With even state-of-the art systems, humans need to review the outputs being delivered and determine how to use the information and for what purpose. In contrast, Osaro’s deep reinforcement learning (DRL) technology, which combines deep learning for perception and reinforcement learning for control, allows human mentors to teach machines how to autonomously take actions to achieve high-level goals.

Naive reinforcement learning techniques attempt to teach machines how to do something from scratch using a “trial-and-error” approach, which is prohibitively time consuming. For example, a machine can be programmed to figure out how to play a video game, but it might take millions of games before it understands the rules and physics enough to play well. Osaro’s technology dramatically reduces the machine’s learning curve by introducing human expertise into the equation -- having a human first demonstrate how to play the game for the machine, helping it comprehend the basic rules to focus more keenly on mastering the end goal.

By helping machines make autonomous decisions and master new skills several orders of magnitude faster than existing schemes, this technology makes Osaro’s products both scalable and easy to deploy in real-world settings. Industrial robotics, for example, will no longer require weeks of custom programing by experts every time a new assembly task is introduced into the production line. Instead, human operators will be able to collaborate and rapidly teach robots how to perform new tasks, making both more productive.

“We can compare the problem of learning complex control to teaching a child how to ride a bike,” said Dr. Arel. “Existing DRL architectures attempt to learn from a blank slate, which is like providing a bike to a child and walking away -- hoping the child will eventually develop bike-riding proficiency through pure trial-and-error. In the real world, we would guide the child. We would work with them on the basic mechanics through hands-on demonstration and practical assistance until they can reliably practice and improve their skill on their own. At its heart, this is our approach and what we believe is the key to successfully deploying machine learning in complex, real-world applications -- helping computers quickly master new skills the same way people learn, and intelligently adapt to a variety of situations.”

Commercial Implications

Osaro is exploring several commercial areas to introduce its deep reinforcement learning technology, including industrial robotics, where the company has been in talks with several leading manufacturers to offer next-generation intelligent robotics solutions. Other domains that would benefit from Osaro’s approach to machine learning include autonomous vehicles and drones, software development tools, automated healthcare systems, virtual personal assistants, online advertising, and mobile gaming.

“Most machine learning companies are still focused on exploring deep learning, which addresses only part of the problem: enabling machines to perceive,” co-founder Derik Pridmore added. Pridmore, who worked as a VC alongside Peter Thiel at Founders Fund, was one of the earliest investors in the field of deep learning and drove Founders Fund’s investment in DeepMind. “That’s important, but most industries need more. Osaro’s deep reinforcement learning technology fills this gap -- we enable products that move beyond merely processing images to automatically take informed, complex actions.”

World-Class Team

To help develop their next-generation machine learning systems, Osaro has assembled an expert team that includes researchers from technology giants such as Apple, eBay, and IBM and advisors who are leaders in the field. Dr. Arel is a serial entrepreneur and accomplished academic. Prior to Osaro he was founder and CTO of Binatix, an early and profitable deep learning company. Dr. Arel has been a professor of electrical engineering and computer science at the University of Tennessee, where he directed the Machine Intelligence Lab, and was previously a visiting associate professor at Stanford University’s Computer Science Department.

Together with Pridmore, who serves as president and COO, Dr. Arel co-founded the company with Michael Kahane, the company’s CTO. Prior to founding Osaro, Kahane held senior engineering positions at Samsung.

Osaro’s technical advisors include Prof. Richard Sutton, a world-renowned expert and pioneer in the fields of artificial intelligence and reinforcement learning, and Prof. Benjamin Van Roy, a leading researcher of computational models for decision making under uncertainty at Stanford University.

“Osaro is offering a pragmatic approach for applying reinforcement learning to real-world problems,” said Prof. Sutton.

“The most impressive machine intelligence demonstration doesn’t amount to anything unless the technology can serve real-world, commercial applications,” said Osaro investor Scott Banister. “Learning from tabula rasa is often not practical - it can’t scale to tackle real problems in an efficient and timely way, which is why Osaro’s approach is so brilliant and elegant.”

About Osaro

Osaro is a San Francisco-based artificial intelligence company developing products based on proprietary deep reinforcement learning technology. The company’s patent pending technology automatically processes large amounts of unstructured data and efficiently learns complex control tasks.